
# import all packages and set plots to be embedded inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb

%matplotlib inline
sb.set_style("darkgrid")


# load in the dataset into a pandas dataframe, print the head
prosper_loan = pd.read_csv('prosperLoanData.csv')
prosper_loan.head(10)


#filter the Dataset to the coulmn that i want:
prosper_loan.columns

Index(['ListingKey', 'ListingNumber', 'ListingCreationDate', 'CreditGrade',
       'Term', 'LoanStatus', 'ClosedDate', 'BorrowerAPR', 'BorrowerRate',
       'LenderYield', 'EstimatedEffectiveYield', 'EstimatedLoss',
       'EstimatedReturn', 'ProsperRating (numeric)', 'ProsperRating (Alpha)',
       'ProsperScore', 'ListingCategory (numeric)', 'BorrowerState',
       'Occupation', 'EmploymentStatus', 'EmploymentStatusDuration',
       'IsBorrowerHomeowner', 'CurrentlyInGroup', 'GroupKey',
       'DateCreditPulled', 'CreditScoreRangeLower', 'CreditScoreRangeUpper',
       'FirstRecordedCreditLine', 'CurrentCreditLines', 'OpenCreditLines',
       'TotalCreditLinespast7years', 'OpenRevolvingAccounts',
       'OpenRevolvingMonthlyPayment', 'InquiriesLast6Months', 'TotalInquiries',
       'CurrentDelinquencies', 'AmountDelinquent', 'DelinquenciesLast7Years',
       'PublicRecordsLast10Years', 'PublicRecordsLast12Months',
       'RevolvingCreditBalance', 'BankcardUtilization',
       'AvailableBankcardCredit', 'TotalTrades',
       'TradesNeverDelinquent (percentage)', 'TradesOpenedLast6Months',
       'DebtToIncomeRatio', 'IncomeRange', 'IncomeVerifiable',
       'StatedMonthlyIncome', 'LoanKey', 'TotalProsperLoans',
       'TotalProsperPaymentsBilled', 'OnTimeProsperPayments',
       'ProsperPaymentsLessThanOneMonthLate',
       'ProsperPaymentsOneMonthPlusLate', 'ProsperPrincipalBorrowed',
       'ProsperPrincipalOutstanding', 'ScorexChangeAtTimeOfListing',
       'LoanCurrentDaysDelinquent', 'LoanFirstDefaultedCycleNumber',
       'LoanMonthsSinceOrigination', 'LoanNumber', 'LoanOriginalAmount',
       'LoanOriginationDate', 'LoanOriginationQuarter', 'MemberKey',
       'MonthlyLoanPayment', 'LP_CustomerPayments',
       'LP_CustomerPrincipalPayments', 'LP_InterestandFees', 'LP_ServiceFees',
       'LP_CollectionFees', 'LP_GrossPrincipalLoss', 'LP_NetPrincipalLoss',
       'LP_NonPrincipalRecoverypayments', 'PercentFunded', 'Recommendations',
       'InvestmentFromFriendsCount', 'InvestmentFromFriendsAmount',
       'Investors'],
      dtype='object')


choozen_col = ['ListingNumber', 'ListingCreationDate','ListingCategory (numeric)', 'Term', 'LoanOriginalAmount', 
               'MonthlyLoanPayment', 'EstimatedLoss','LenderYield', 'LoanStatus', 'ProsperRating (Alpha)', 'ProsperScore', 
               'EmploymentStatus', 'IncomeRange', 'StatedMonthlyIncome']
loan_filter = prosper_loan[choozen_col]
loan_filter


loan_filter.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 14 columns):
 #   Column                     Non-Null Count   Dtype  
---  ------                     --------------   -----  
 0   ListingNumber              113937 non-null  int64  
 1   ListingCreationDate        113937 non-null  object 
 2   ListingCategory (numeric)  113937 non-null  int64  
 3   Term                       113937 non-null  int64  
 4   LoanOriginalAmount         113937 non-null  int64  
 5   MonthlyLoanPayment         113937 non-null  float64
 6   EstimatedLoss              84853 non-null   float64
 7   LenderYield                113937 non-null  float64
 8   LoanStatus                 113937 non-null  object 
 9   ProsperRating (Alpha)      84853 non-null   object 
 10  ProsperScore               84853 non-null   float64
 11  EmploymentStatus           111682 non-null  object 
 12  IncomeRange                113937 non-null  object 
 13  StatedMonthlyIncome        113937 non-null  float64
dtypes: float64(5), int64(4), object(5)
memory usage: 12.2+ MB


#change the datatype
loan_filter['ListingNumber'] = loan_filter['ListingNumber'].astype('object')

C:\Users\youdi\AppData\Local\Temp\ipykernel_15864\455395077.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  loan_filter['ListingNumber'] = loan_filter['ListingNumber'].astype('object')


loan_filter.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 14 columns):
 #   Column                     Non-Null Count   Dtype  
---  ------                     --------------   -----  
 0   ListingNumber              113937 non-null  object 
 1   ListingCreationDate        113937 non-null  object 
 2   ListingCategory (numeric)  113937 non-null  int64  
 3   Term                       113937 non-null  int64  
 4   LoanOriginalAmount         113937 non-null  int64  
 5   MonthlyLoanPayment         113937 non-null  float64
 6   EstimatedLoss              84853 non-null   float64
 7   LenderYield                113937 non-null  float64
 8   LoanStatus                 113937 non-null  object 
 9   ProsperRating (Alpha)      84853 non-null   object 
 10  ProsperScore               84853 non-null   float64
 11  EmploymentStatus           111682 non-null  object 
 12  IncomeRange                113937 non-null  object 
 13  StatedMonthlyIncome        113937 non-null  float64
dtypes: float64(5), int64(3), object(6)
memory usage: 12.2+ MB


# change ListingCreationDate to datetime dtype
loan_filter['ListingCreationDate'] = pd.to_datetime(loan_filter['ListingCreationDate'])

C:\Users\youdi\AppData\Local\Temp\ipykernel_15864\3590065032.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  loan_filter['ListingCreationDate'] = pd.to_datetime(loan_filter['ListingCreationDate'])


loan_filter[loan_filter['ProsperScore'].isna()].ListingCreationDate.dt.year.value_counts()

2007    11557
2008    11263
2006     6213
2009       28
2005       23
Name: ListingCreationDate, dtype: int64


loan_filter = loan_filter[loan_filter['ListingCreationDate'] >= '07/01/2009'].reset_index()
loan_filter.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 84853 entries, 0 to 84852
Data columns (total 15 columns):
 #   Column                     Non-Null Count  Dtype         
---  ------                     --------------  -----         
 0   index                      84853 non-null  int64         
 1   ListingNumber              84853 non-null  object        
 2   ListingCreationDate        84853 non-null  datetime64[ns]
 3   ListingCategory (numeric)  84853 non-null  int64         
 4   Term                       84853 non-null  int64         
 5   LoanOriginalAmount         84853 non-null  int64         
 6   MonthlyLoanPayment         84853 non-null  float64       
 7   EstimatedLoss              84853 non-null  float64       
 8   LenderYield                84853 non-null  float64       
 9   LoanStatus                 84853 non-null  object        
 10  ProsperRating (Alpha)      84853 non-null  object        
 11  ProsperScore               84853 non-null  float64       
 12  EmploymentStatus           84853 non-null  object        
 13  IncomeRange                84853 non-null  object        
 14  StatedMonthlyIncome        84853 non-null  float64       
dtypes: datetime64[ns](1), float64(5), int64(4), object(5)
memory usage: 9.7+ MB


#add a non numaric listing category
category = {0:'Not Available', 1:'Debt Consolidation', 2:'Home Improvement', 3:'Business', 4:'Personal Loan', 
            5:'Student Use', 6:'Auto', 7:'Other', 8:'Baby&Adoption', 9:'Boat', 10:'Cosmetic Procedure', 
            11:'Engagement Ring', 12:'Green Loans', 13:'Household Expenses', 14:'Large Purchases', 15:'Medical/Dental', 
            16:'Motorcycle', 17:'RV', 18:'Taxes', 19:'Vacation', 20:'Wedding Loans'}
category_list = []
for num in loan_filter['ListingCategory (numeric)']:
    category_list.append(category[num])
loan_filter['ListingCategory'] = category_list
loan_filter.drop(columns='ListingCategory (numeric)', inplace=True)


#change ProsperRating (Alpha) and IncomeRange dtype and remove the space from columns name:
order_dict = {'ProsperRating (Alpha)':['HR', 'E', 'D', 'C', 'B', 'A', 'AA'], 
              'IncomeRange': ['Not employed', '$0', '$1-24,999', '$25,000-49,999', 
                              '$50,000-74,999', '$75,000-99,999', '$100,000+']}
for col in order_dict:
    loan_filter[col] = loan_filter[col].astype(
                  pd.api.types.CategoricalDtype(categories=order_dict[col], ordered=True))

loan_filter.rename(columns = {'ProsperRating (Alpha)':'ProsperRating'}, inplace = True)


loan_filter.describe()


loan_filter.columns

Index(['index', 'ListingNumber', 'ListingCreationDate', 'Term',
       'LoanOriginalAmount', 'MonthlyLoanPayment', 'EstimatedLoss',
       'LenderYield', 'LoanStatus', 'ProsperRating', 'ProsperScore',
       'EmploymentStatus', 'IncomeRange', 'StatedMonthlyIncome',
       'ListingCategory'],
      dtype='object')


# I add a profit columns
loan_filter['profit($)'] = loan_filter.LenderYield * loan_filter.LoanOriginalAmount


fig, axes = plt.subplots(2, 2, figsize=[12, 10], dpi=250)
fig.suptitle('Lender Yield Distribution', fontsize=14)

sb.histplot(data=loan_filter, x='LenderYield', ax=axes[0][0])
sb.boxplot(data=loan_filter, x='LenderYield', ax=axes[0][1])
sb.histplot(data=loan_filter, x='profit($)', ax=axes[1][0])
sb.boxplot(data=loan_filter, x='profit($)', ax=axes[1][1])


axes[0][0].set_title('Lender Yield Histogram Plot')
axes[0][1].set_title('Lender Yield Box Plot')
axes[1][0].set_title('Profit (lender yield * loan original amount) Histogram Plot')
axes[1][1].set_title('Profit Box Plot')
axes[0][0].set_xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5], [0, '10%', '20%', '30%', '40%', '50%'])
axes[0][1].set_xticks([0, 0.1, 0.2, 0.3, 0.4, 0.5], [0, '10%', '20%', '30%', '40%', '50%'])
plt.savefig("images/LenderYield.png");


# bar plot for the 10 frequents Purpose Of The Loan
def plot1(data, y, y_label, title):
    "ploting ordered count plot of y"
    listing_order = list(data[y].value_counts().index)[:10]
    plt.figure(figsize=[12,7])
    sb.countplot(data=data, y=y, order=listing_order, palette='mako')
    plt.title(title, fontsize=16)
    plt.ylabel(y_label);
plot1(data=loan_filter, y='ListingCategory', y_label='Purpose Of The Loan', title='10 frequents Purpose Of The Loan')


import re
fig, axes = plt.subplots(1, 3, figsize=(12, 6), dpi=300)
fig.suptitle('Refundability', fontsize=16)
fig.supylabel('Count')

for i, col in enumerate(['ProsperRating', 'IncomeRange', 'EmploymentStatus']):
    sb.countplot(data=loan_filter, x=col, palette="mako",ax=axes[i])

    axes[i].bar_label(axes[i].containers[0])


    axes[i].set_yticks([])
    axes[i].set(yticklabels=[])


    axes[i].set_xlabel('')
    axes[i].set_ylabel('')


    axes[i].set_title(f"{' '.join(re.findall('[A-Z][^A-Z]*', col))} count plot")
    axes[i].tick_params(axis='x', labelrotation=60)

plt.tight_layout()
axes[0].tick_params(axis='x', labelrotation=0)
plt.savefig("images/Refundability");


plt.figure(figsize=[12, 6], dpi=250)

plt.subplot(1,2,1)
sb.histplot(data=loan_filter, x='LoanOriginalAmount', bins=30, kde=True)
plt.xlim(0)
plt.xlabel('Loan Original Amount($)')
plt.title('Loan Original Amount histogram plot')
plt.subplot(1,2,2)
sb.histplot(data=loan_filter, x='LoanOriginalAmount', log_scale=True, bins=30, kde=True)
plt.xticks([1e3, 2e3, 5e3, 1e4, 2e4], ["1k", "2k", "5k", "10k", "20k"])
plt.xlabel('Loan Original Amount($)')
plt.title('Loan Original Amount histogram - log scale');


refundability_vars = ['ProsperRating', 'IncomeRange',  'EmploymentStatus']
numerical_vars = ['Term', 'LoanOriginalAmount', 'EstimatedLoss', 'ProsperScore', 'LenderYield', 
                  'MonthlyLoanPayment', 'StatedMonthlyIncome']


fig, axes = plt.subplots(3, 2, figsize=[8, 8], dpi=250)
fig.suptitle('Refundability Vs Lender Yield', fontsize=16)


for i, col in enumerate(refundability_vars):
    sb.barplot(data=loan_filter, x=col, y='LenderYield', ax=axes[i][0], palette='mako')
    sb.boxplot(data=loan_filter, y='LenderYield', x=col, ax=axes[i][1], palette='RdPu')
    axes[i][1].set_ylabel('')
    axes[i][0].tick_params(axis='x', labelrotation=60)
    axes[i][1].tick_params(axis='x', labelrotation=60)
    axes[i][0].set_yticks([0, 0.1, 0.2, 0.3, 0.4], [0, '10%', '20%', '30%', '40%'])
    axes[i][1].set_yticks([0, 0.1, 0.2, 0.3, 0.4], [0, '10%', '20%', '30%', '40%'])
plt.tight_layout()
plt.savefig("images/Ref_len");


fig, axes = plt.subplots(3, 2, figsize=[8, 8], dpi=250)
fig.suptitle('Refundability Vs Profit', fontsize=16)


for i, col in enumerate(refundability_vars):
    sb.barplot(data=loan_filter, x=col, y='profit($)', ax=axes[i][0], palette='mako')
    sb.boxplot(data=loan_filter, y='profit($)', x=col, ax=axes[i][1], palette='RdPu')
    axes[i][1].set_ylabel('')
    axes[i][0].tick_params(axis='x', labelrotation=60)
    axes[i][1].tick_params(axis='x', labelrotation=60)
plt.tight_layout()


fig, axes = plt.subplots(2, 1, figsize=[10, 5], dpi=250)
fig.suptitle('Refundability Vs LoanOriginalAmount', fontsize=12)


for i, col in enumerate(['ProsperRating', 'IncomeRange']):
    sb.barplot(data=loan_filter, x=col, y='LoanOriginalAmount', ax=axes[i], palette='mako')
    axes[i].tick_params(axis='x', labelrotation=60)
    axes[i].set_ylabel('Loan Original Amount($)')


plt.figure(figsize=[9, 2], dpi=300)

yield_corr = pd.DataFrame(loan_filter[numerical_vars].corr()['LenderYield']).transpose().drop(columns='LenderYield')
plt.title('Correlation Between Lender Yield And Numerical Variables.')
plt.xticks(rotation=30)
sb.heatmap(yield_corr, annot=True, center=0, cmap='twilight_shifted', fmt='.2f');


plt.figure(figsize=[18, 7], dpi=300)
plt.suptitle('Estimated Loss Vs Lender Yield', fontsize=16)
plt.subplot(1, 2, 1)
plt.scatter(data=loan_filter, x='EstimatedLoss', y='LenderYield', alpha=0.05)
plt.ylabel('Lender Yield')
plt.xlabel('Estimated Loss')
plt.subplot(1 , 2, 2)
plt.hist2d(data=loan_filter, x='EstimatedLoss', y='LenderYield', cmap='plasma_r', bins=[20, 20], cmin=0.5)
plt.colorbar()
plt.xlabel('Estimated Loss');


plt.figure(figsize=[6, 6])
sb.kdeplot(data=loan_filter, x='ProsperScore', y='LenderYield', shade=True, thresh=0.05, cbar=True)
plt.title('Prosper Score Vs Lender Yield');


fig, axes = plt.subplots(3, 2, figsize=[8, 8], dpi=250)
fig.suptitle('Estimated Loss Vs Refundability', fontsize=16)


for i, col in enumerate(refundability_vars):
    sb.barplot(data=loan_filter, x=col, y='EstimatedLoss', ax=axes[i][0], palette='mako')
    sb.boxplot(data=loan_filter, y='EstimatedLoss', x=col, ax=axes[i][1], palette='RdPu')
    axes[i][1].set_ylabel('')
    axes[i][0].tick_params(axis='x', labelrotation=60)
    axes[i][1].tick_params(axis='x', labelrotation=60)
    axes[i][0].set_yticks([0, 0.1, 0.2, 0.3, 0.4], [0, '10%', '20%', '30%', '40%'])
    axes[i][1].set_yticks([0, 0.1, 0.2, 0.3, 0.4], [0, '10%', '20%', '30%', '40%'])
plt.tight_layout()


loan_filter['month_year'] = pd.to_datetime(loan_filter['ListingCreationDate']).dt.to_period('M')
mean_loan_month = pd.DataFrame(loan_filter.groupby('month_year')['LenderYield'].mean()).reset_index()


plt.figure(figsize=[9, 4], dpi=300)
sb.lineplot(x=mean_loan_month['month_year'].astype(str), y=mean_loan_month['LenderYield'])
plt.xticks(rotation=90)
plt.title('Lender Yield over time');


plt.figure(figsize=[9, 4], dpi=300)
sb.scatterplot(data=loan_filter, x='EstimatedLoss', y='LenderYield', alpha=0.05, hue='ProsperRating', palette='RdYlGn')
plt.title('Comparing Lender Yield And Estimated Loss With Prosper Rating')
plt.xticks([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35], [0, '5%', '10%', '15%', '20%', '25%', '30%', '35%'])
plt.yticks([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35], [0, '5%', '10%', '15%', '20%', '25%', '30%', '35%'])
plt.savefig("images/multy");


def hist2dgrid(x, y, **kwargs):
    """ Quick hack for creating heat maps with seaborn's PairGrid. """
    palette = kwargs.pop('color')
    plt.hist2d(x, y, bins = [15, 15], cmap = palette, cmin = 0.3)
    plt.xticks([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35], [0, '5%', '10%', '15%', '20%', '25%', '30%', '35%'])
    plt.yticks([0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35], [0, '5%', '10%', '15%', '20%', '25%', '30%', '35%']);


g = sb.FacetGrid(data = loan_filter, col = 'ProsperRating', col_wrap=4)
g.map(hist2dgrid, 'EstimatedLoss', 'LenderYield', color = 'plasma_r')
g.set_xlabels('Estimated Loss')
g.set_ylabels('Lender Yield');


fig = plt.figure(figsize = [6,6], dpi=150)
ax = sb.pointplot(data = loan_filter, x = 'ProsperRating', y = 'LenderYield', hue = 'IncomeRange',
           palette = 'Blues', linestyles = '', dodge = 0.4)
plt.title('Lender Yield Across Prosprer Raitind And Clarity');

	ListingKey	ListingNumber	ListingCreationDate	CreditGrade	Term	LoanStatus	ClosedDate	BorrowerAPR	BorrowerRate	LenderYield	...	LP_ServiceFees	PercentFunded	Investors
0	1021339766868145413AB3B	193129	2007-08-26 19:09:29.263000000	C	36	Completed	2009-08-14 00:00:00	0.16516	0.1580	0.1380	...	-133.18	1.0	258
1	10273602499503308B223C1	1209647	2014-02-27 08:28:07.900000000	NaN	36	Current	NaN	0.12016	0.0920	0.0820	...	0.00	1.0	1
2	0EE9337825851032864889A	81716	2007-01-05 15:00:47.090000000	HR	36	Completed	2009-12-17 00:00:00	0.28269	0.2750	0.2400	...	-24.20	1.0	41
3	0EF5356002482715299901A	658116	2012-10-22 11:02:35.010000000	NaN	36	Current	NaN	0.12528	0.0974	0.0874	...	-108.01	1.0	158
4	0F023589499656230C5E3E2	909464	2013-09-14 18:38:39.097000000	NaN	36	Current	NaN	0.24614	0.2085	0.1985	...	-60.27	1.0	20
5	0F05359734824199381F61D	1074836	2013-12-14 08:26:37.093000000	NaN	60	Current	NaN	0.15425	0.1314	0.1214	...	-25.33	1.0	1
6	0F0A3576754255009D63151	750899	2013-04-12 09:52:56.147000000	NaN	36	Current	NaN	0.31032	0.2712	0.2612	...	-22.95	1.0	1
7	0F1035772717087366F9EA7	768193	2013-05-05 06:49:27.493000000	NaN	36	Current	NaN	0.23939	0.2019	0.1919	...	-69.21	1.0	1
8	0F043596202561788EA13D5	1023355	2013-12-02 10:43:39.117000000	NaN	36	Current	NaN	0.07620	0.0629	0.0529	...	-16.77	1.0	1
9	0F043596202561788EA13D5	1023355	2013-12-02 10:43:39.117000000	NaN	36	Current	NaN	0.07620	0.0629	0.0529	...	-16.77	1.0	1

	ListingNumber	ListingCreationDate	ListingCategory (numeric)	Term	LoanOriginalAmount	MonthlyLoanPayment	EstimatedLoss	LenderYield	LoanStatus	ProsperRating (Alpha)	ProsperScore	EmploymentStatus	IncomeRange	StatedMonthlyIncome
0	193129	2007-08-26 19:09:29.263000000	0	36	9425	330.43	NaN	0.1380	Completed	NaN	NaN	Self-employed	$25,000-49,999	3083.333333
1	1209647	2014-02-27 08:28:07.900000000	2	36	10000	318.93	0.0249	0.0820	Current	A	7.0	Employed	$50,000-74,999	6125.000000
2	81716	2007-01-05 15:00:47.090000000	0	36	3001	123.32	NaN	0.2400	Completed	NaN	NaN	Not available	Not displayed	2083.333333
3	658116	2012-10-22 11:02:35.010000000	16	36	10000	321.45	0.0249	0.0874	Current	A	9.0	Employed	$25,000-49,999	2875.000000
4	909464	2013-09-14 18:38:39.097000000	2	36	15000	563.97	0.0925	0.1985	Current	D	4.0	Employed	$100,000+	9583.333333
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
113932	753087	2013-04-14 05:55:02.663000000	1	36	10000	364.74	0.0699	0.1764	Current	C	5.0	Employed	$50,000-74,999	4333.333333
113933	537216	2011-11-03 20:42:55.333000000	7	36	2000	65.57	0.0200	0.1010	FinalPaymentInProgress	A	8.0	Employed	$75,000-99,999	8041.666667
113934	1069178	2013-12-13 05:49:12.703000000	1	60	10000	273.35	0.1025	0.2050	Current	D	3.0	Employed	$25,000-49,999	2875.000000
113935	539056	2011-11-14 13:18:26.597000000	2	60	15000	449.55	0.0850	0.2505	Completed	C	5.0	Full-time	$25,000-49,999	3875.000000
113936	1140093	2014-01-15 09:27:37.657000000	1	36	2000	64.90	0.0299	0.0939	Current	A	7.0	Employed	$50,000-74,999	4583.333333

	index	Term	LoanOriginalAmount	MonthlyLoanPayment	EstimatedLoss	LenderYield	ProsperScore	StatedMonthlyIncome
count	84853.000000	84853.000000	84853.000000	84853.000000	84853.000000	84853.000000	84853.000000	8.485300e+04
mean	56958.246850	42.486135	9083.440515	291.930720	0.080306	0.186017	5.950067	5.931175e+03
std	32885.220638	11.640346	6287.860058	186.678314	0.046764	0.074631	2.376501	8.239944e+03
min	1.000000	12.000000	1000.000000	0.000000	0.004900	0.030000	1.000000	0.000000e+00
25%	28507.000000	36.000000	4000.000000	157.330000	0.042400	0.125900	4.000000	3.434000e+03
50%	56960.000000	36.000000	7500.000000	251.940000	0.072400	0.177500	6.000000	5.000000e+03
75%	85420.000000	60.000000	13500.000000	388.350000	0.112000	0.247400	8.000000	7.083333e+03
max	113936.000000	60.000000	35000.000000	2251.510000	0.366000	0.340000	11.000000	1.750003e+06

Part I - Prosper Loan Data¶

by Yehudit Yeret¶

Preliminary Wrangling¶

What is the structure of your dataset?¶

What is/are the main feature(s) of interest in your dataset?¶

What features in the dataset do you think will help support your investigation into your feature(s) of interest?¶

Univariate Exploration¶

Lender Yield Distribution¶

The Purpose Of The Loan Bar Chart¶

Refundability - ProsperRating, EmploymentStatus, IncomeRange¶

Loan Original Amount Distribution¶

Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶

Bivariate Exploration¶

Lender Yield And Refundability¶

Profit And Refundability¶

Refundability Vs LoanOriginalAmount¶

Correlation Between Lender Yield And Numerical Variables.¶

Estimated Loss And Refundability¶

Lender Yield over time¶

Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶

Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?¶

Multivariate Exploration¶

Comparing Lender Yield And Estimated Loss With Prosper Rating.¶

Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶

Conclusions¶